I’m very new to R (and statistics) and I searched a lot for a possible solution, but couldn’t find any.

I have a data set with around 18000 entries, which contain two columns: "rentals" and "season". I want to analyse if there is a difference between the mean of the rentals depending on the season using an one-way ANOVA.

My data looks like this:

First I calculate the SD and MEAN of the groups (season):

anova %>%
    group_by(season) %>%
    summarise(
        count_season = n(),
        mean_rentals = mean(rentals, na.rm = TRUE),
        sd_rentals = sd(rentals, na.rm = TRUE))

This is the result:

Then I perform the one-way ANOVA:

anova_one_way <- aov(season~as.factor(rentals), data = anova)
summary(anova_one_way)
<!-- I use "as.factor" on rentals, because otherwise I'm getting an error with TukeyHSD -->

Result:

Here comes the tricky part. I perform a TukeyHSD test:

TukeyHSD(anova_one_way) 

And the results are very disappointing. TukeyHSD returns 376896 rows, while I expect it to return just a few, comparing the seasons with each other. It looks like every single "rentals" row is being handled as a single group. This seems to be very wrong but I can’t find the cause. Is this a common TukeyHSD behaviour considering the big data set or is there an error in my code or logic, which causes this enormous unreadable list of values as a return?

Here is a small insight on how it looks like (and it goes on until 376896).

>Solution :

The terms are the wrong way around in your aov() call. Rentals is the outcome (dependent) variable, season is the predictor (independent) variable.

So you want:

anova_one_way <- aov(rentals ~ factor(season), data = anova)